Bacteria classification using image processing and deep learning. With advances in image processing, specifically, the use .

Bacteria classification using image processing and deep learning. The aim of this paper is to bring the new approach to classify the species of bacteria from Digital Image of Bacterial Species (DIBaS) by using Image Processing and Deep Convolutional Neural Network model to reduce the time consumption and increase the classification accuracy in traditional way. Deep learning is famous for its accuracy and ability to extract low-level features from the images. Our approach utilizes the well-established ResNet-50 convolutional neural network (CNN) architecture, which has been pre-trained, to classify digital images of bacteria into 33 distinct categories. The developed approach for image classification shows very promising results. Jan 30, 2021 · Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. We believe that deep-learning technology Contribute to azizul0332/Bacteria_Classification_using_Image_Processing_and_Deep_learning development by creating an account on GitHub. It encompasses an extensive collection of 18,000 high-resolution images depicting five distinct microorganisms, meticulously curated as single or mixed cultures, taken under diverse lighting conditions using Bacterial classification is a vital step in medical diagnosis. It is mainly used for processing and recognition of image. pp. Moreover, in case of diagnostic uncertainty (the misleading similarity in shape or structure of bacterial cells), such Contribute to azizul0332/Bacteria_Classification_using_Image_Processing_and_Deep_learning development by creating an account on GitHub. tr@kmitl. To gather information on the application of Feb 15, 2025 · To overcome this, statistical approaches, including principal component analysis, have been used for bacterial classification [16]. Jan 1, 2023 · We developed and tested two algorithms (using image processing an Casual Probabilistic Network (CPN) and a Random Forest (RF) classification) for the automated classification of Gram stain images. Much research has been carried out on bacteria classification using machine learning algorithms. A deep learning approach employing a CNN for image classification is presented in this paper. 2019. Manual classification of bacteria Aug 11, 2025 · As a result, the medical device industry has grown in recent years through deep learning techniques and the use of most research related to diagnosis and image processing. Contribute to azizul0332/Bacteria_Classification_using_Image_Processing_and_Deep_learning development by creating an account on GitHub. The accuracy, then proposed method is used for improving the system. An automizing process for bacteria recognition becomes attractive to reduce the analyzing time and increase the accuracy of diagnostic process. Thus, during this paper, we've investigated an approach to automate the method of bacteria recognition and classification with the utilization of deep convolutional neural network (DCNN). The classification of bacterial species with the help of computer-aided systems would provide great convenience for biologists. th, 2 suvit@pit. For segmentation, the U-Net algorithm is employed to detect positive/negative classifications of bacteria. ac. Experimental results demonstrate that our method outperforms other classical methods on segmentation and classification. Nov 13, 2023 · Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. Different image processing approaches, such as traditional machine learning methods and modern deep learning The experimental results compare the deep learning methodology for accuracy in bacteria recognition standard resolution image use case. The approach is useful in automatically labeling a certain bacteria division after detecting and segmenting (extracting) individual bacteria images from microscopic images of colonies. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. In response to this challenge, we propose a transformer-based neural network for open-set bacterial recognition using SERS spectra. Apr 18, 2024 · Download Citation | On Apr 18, 2024, Sunanda and others published Classification of Bacteria from Agar Plate Using Deep Learning and Image Processing | Find, read and cite all the research you Sep 14, 2017 · In microbiology it is diagnostically useful to recognize various genera and species of bacteria. This comprehensive review analyzes progress in bacterial image classification, emphasizing deep learning methodologies, image processing methods, and computer models utilized in clinical microbiology. Dec 15, 2024 · Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. 1109/itc-cscc. 5). This research study possibility to use image classification and deep learning method for classify genera of bacteria. Abstract An automizing process for bacteria recognition becomes attractive to reduce the analyzing time and increase the accuracy of diagnostic process. Feb 14, 2024 · Bacteria classification using image processing and deep learning. Jun 1, 2019 · Since deep learning enables multi-class image classification, it also potentially classifies the differences in bacteria based on the different morphology presented for each species, gram By leveraging CNNs and integrating advanced image processing techniques, our approach promises to enhance the accuracy and efficiency of bacteria classification, thereby advancing medical diagnoses and treatments. th 1 Abstract The purpose Jul 10, 2020 · In summary, we presented a deep learning-based live bacteria monitoring system for the early detection of growing colonies and the classification of colony species using deep learning. This research presents an artificial intelligence (AI)-based approach for the classification of filamentous and floc-forming bacteria in microscopic images using deep learning. We have evaluated our algorithm using a dataset of 22 sputum smear microscopic images with different backgrounds (high density and low-density images). Abstract- This work presents the concept of bacteria image classification using ResNet and proposed AlexNet based deep learning method. Nov 1, 2020 · Request PDF | Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models | Classification of bacteria is essential in the medical diagnosis of infectious This article applies the state of the art method for texture analysis to classify genera and species of bacteria using deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. Using machine learning, an automated bacteria species classification system can provide an efficient and Jul 30, 2022 · Abstract and Figures Using machine vision and image processing methods has an important role in the identification of defects of agricultural products, especially potatoes. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even This paper aims to review the published research works, which investigate the automatic classification of bacterial colonies via computer vision and machine learning techniques. We develop the model using one of the deep learning methods CNN to detect the crop disease. Oct 19, 2023 · A range of CNN architectures have been developed and effectively applied in the realm of image classification. We believe that deep-learning technology Jun 8, 2024 · Treebupachatsakul, T. May 14, 2025 · This study introduces the Annotated Germs for Automated Recognition (AGAR) dataset, an image database designed to advance the automation of microbial colony detection and classification. . One of the advantages of CNN is it predicts the features of an input image automatically without involvement of humans. The dataset includes various pathogenic fungi from the Lasiodiplodia genus, recognized for their high virulence [28] and association with Grapevine Trunk Diseases (GTD). This work mainly focus on bacteria image classification based on deep learning method. The implementation results have confirmed that bacteria images from microscope are able to recognize the genus of bacterium, and the deep learning methodology for accuracy in bacteria recognition standard resolution image use case is compared. Oct 1, 2024 · Automation of image analysis of the Gram-stained specimens could help in early detection [5]. The most common | Find, read and cite all the research you Jul 1, 2023 · Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. 1–3. This paper aims to facilitate such a troublesome task using deep learning techniques. 8793320 The experimental results compare the deep learning methodology for accuracy in bacteria recognition standard resolution image use case. Steps in classical image processing consist of RGB to HSV converting, HSV thresholding, morphological operation and bacilli counting. Jan 1, 2018 · The proposed method performs detection of TB, by image binarization and subsequent classification of detected regions using a convolutional neural network. This is an all-inclusive method for automatically classifying bacteria on agar plates by combining image processing techniques with deep learning methods. Recently, our group has reported coffee-ring effect assisted label-free SERS analysis method for selective detection of fire blight bacteria using a pathogen-specific bacteriophage [17]. Dec 16, 2022 · In this paper, we review the publications, which investigate the discrimination between bacteria genera and suborders based on macroscopic images via image processing and machine learning methods. Jul 28, 2023 · Most bacteria can be grown on culture media. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. Besides, the newest generation of convolutional neural networks (CNN) have achieved impressive leads to the sector of image classification recently. Traditionally, a bacterial colony expert inspects the sample to determine the type of bacteria through visual inspection or molecular biology techniques. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. The CNN and DNN system has a problem with accuracy, then proposed method is used for improving the system. In: 2019 34th international technical conference on circuits/systems, computers and communications (ITC-CSCC). Here, we are proposing an automated classification method based on deep learning. A large number of studies are published annually on deep learning techniques. The extracted features are then fed into an SVM classifier to classify the images into different bacterial species. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. Oct 1, 2023 · Therefore, an automated bacterial recognition and classification approach is required compared to a challenging and time-consuming manual process. The results indicated that high resolution images (788 × 530) resulted in better overall segmentation, as evidenced by the improved detection of free and connected filamentous bacteria (Fig. This procedure normally has several stages. It can be achieved using computer-aided methods, which make the recognition processes more automatic and thus significantly reduce the time necessary for the classification. Jan 1, 2020 · The possibility of using image classification and deep learning methods to recognize the standard and high-resolution bacteria and yeast images is studied in Treebupachatsakul and Poomrittigul (2020). Keywords: bacteria division, longitudinal bacterial fission, bacteria classification, deep learning, transfer learning, image processing, image segmentation Aug 23, 2024 · We introduce a digital inline holography (DIH) method combined with deep learning (DL) for real-time detection and analysis of bacteria in liquid suspension. Sep 9, 2015 · Once the foreground patches are identified, we train a supervised deep learning method, Convolutional Neural Network (CNN), that predicts which bacterial colonies from the pool occur in a query image. We evaluate the Dec 13, 2024 · Classifying bacteria species using traditional methods can be both costly and time-consuming due to the hard and rigorous process of biomedical tests. 2019 34th international technical conference on circuits/systems, computers and communications (ITC-CSCC); Piscataway. Nov 29, 2021 · Computer Vision by using digital image processing can be used for plant disease image classification tasks [7]. Treebupachatsakul T, Poomrittigul S. We propose the implementation method of bacteria recognition system using Python programming and the Keras API with TensorFlow Machine Bacteria Classification using Image Processing and Deep learning Treesukon Treebupachatsakul1, Suvit Poomrittigul2 1 Department of Biomedical Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand 2 Department of Software Engineering and Information System, Pathumwan Institute of Technology, Thailand E-mail: treesukon. The experimental results compare the deep learning methodology for accuracy in bacteria recognition standard resolution image use case. This trained ResNet-50 is implemented as the CNN architecture. Other driving methods can be executed with extraordinary focus using this method. coli, yeast, and particles, we perform tests using datasets from a variety of researchers. The performance of deep learning is directly proportional to the size of dataset. This paper is organized into 6 sections. Jun 8, 2021 · Keywords: bacteria division, longitudinal bacterial fission, bacteria classification, deep learning, transfer learning, image processing, image segmentation Citation: Garcia-Perez C, Ito K, Geijo J, Feldbauer R, Schreiber N and zu Castell W (2021) Efficient Detection of Longitudinal Bacteria Fission Using Transfer Learning in Deep Neural Networks. Dec 4, 2019 · Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. 2019. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Jul 1, 2021 · Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. An automizing process for bacteria recognition becomes attractive to reduce the analyzing time and increase the accuracy of diagnostic process. A method and a system for identifying polyculture bacteria on microscopic images using deep learning Nov 13, 2023 · Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Nov 1, 2019 · This work presents twelve fine-tuned deep learning architectures to solve the bacterial classification problem over the Digital Image of Bacterial Species Dataset. Our work can advance the application of deep learning in M-ROSE. Employing the Enhanced CNN model, the study demonstrates the effectiveness of deep learning techniques in image classification on a diverse bacterial species. A dataset of 660 images including 33 microbial species (32 bacteria and one fungus) was split into training, validation, and test sets. May 4, 2022 · The possibility of using image classification and deep learning methods to recognize the standard and high-resolution bacteria and yeast images is studied in Treebupachatsakul and Poomrittigul (2020). Jul 1, 2024 · Deep learning-based image analysis recognizes microbial cells from soils in soil chips. However, due to microbial diversity and high variability in appearance, the manual classification of bacteria is a challenging and time-consuming task. 1 Deep Learning Recent deep learning has emerged in the field of biological image processing and medical image processing. Numerous characteristics, including batch size, edges, shape, and color, support the model's categorization and enhance its capacity to discriminate between Jun 8, 2021 · The approach is useful in automatically labeling a certain bacteria division after detecting and segmenting (extracting) individual bacteria images from microscopic images of colonies. Keywords: bacteria division, longitudinal bacterial fission, bacteria classification, deep learning, transfer learning, image processing, image segmentation Contribute to azizul0332/Bacteria_Classification_using_Image_Processing_and_Deep_learning development by creating an account on GitHub. The authors have analyzed and discussed different image analysis methods used for image pre-processing, feature extraction, post-processing, classification and evaluation. in 2019 34th International Technical Conference on Circuits/Systems, Computers and The research team [11] carried out research on a deep framework for bacterial image segmentation and classification to automatically identify bacteria, classify regions of bacterial colony images, and correspond to them across different images from different contexts. A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches Li et al. Feb 11, 2025 · This research explores the integration of Convolutional Neural Networks (CNNs) for the classification of bacterial samples, aiming to revolutionize the traditional manual classification methods in the medical field. doi:10. Aug 29, 2020 · Treebupachatsakul T, Poomrittigul S (2019) Bacteria classification using image processing and deep learning, 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). The fundamental test of picture de-obscuring is to devise effective and dependable calculations for recuperating however much data as could reasonably be expected from the given information Aug 31, 2021 · To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. May 15, 2025 · However, current deep learning models for bacterial SERS spectra classification typically operate under a closed-set paradigm, limiting their effectiveness when encountering bacterial species outside the training set. The unicellular bacteria reproduce by dividing into two cells, which increases the number of bacteria in the population. In Thailand there is still not enough experts to deal with it so the aim of this This A key component of illness diagnosis is the classification of microorganisms. However, this first study is limited to only two genera of bacteria. Jul 26, 2020 · 1. This research st Jul 9, 2022 · We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth The advent of AI in the field of image classification using different ML techniques gave edge to the different researchers to automate the classification of bacteria based on image analysis. Aug 31, 2023 · This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. Bacteria Classification using Image Processing and Deep learning -Matlab ProjectsDescription ABSTRACT Bacteria classification is an essential task in the medical field, for the diagnosis and treatment of various diseases. We believe that deep-learning technology Feb 19, 2022 · In this study, a method to classify bacteria images by implementing the CNN deep learning method using Transfer Learning is proposed. Proposed method can be applied the high-resolution datasets till standard resolution datasets for prediction bacteria type. It encompasses an extensive collection of 18,000 high-resolution images depicting five distinct microorganisms, meticulously curated as single or mixed cultures, taken under diverse lighting conditions using Contribute to azizul0332/Bacteria_Classification_using_Image_Processing_and_Deep_learning development by creating an account on GitHub. Oct 30, 2019 · Here the authors generate an extensive dataset of bacterial Raman spectra and apply deep learning to identify common bacterial pathogens and predict antibiotic treatment from noisy Raman spectra. Abstract—Deep learning is an area of machine learning that has substantial potential in various fields of study such as image processing and computer vision. This work presents the concept of bacteria image classification using CNN and proposed deep learning method. Pre-trained self-attention based vision transformers have gained popularity in image classification problems because of their strong transfer learning capabilities. With advances in image processing, specifically, the use Jun 8, 2024 · In object detection, the YOLOv5 algorithm is applied for the classification of positive/negative cocci and positive/negative bacilli. Bacteria classification using image processing and deep learning, in 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) (JeJu: ), 1–3. Nov 1, 2023 · PDF | In machine learning, classification is a supervised learning concept which basically categorizes a set of data into classes. Several machine learning based algorithms are implemented in the image analysis procedure for bacteria detection [6], [7]. Bacteria classification using image processing and deep learning. The algorithms were evaluated based on their ability to Dec 4, 2019 · In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. Deep convolutional neural networks have performed remarkably well on computer-aided diagnostics. Mar 31, 2024 · Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images. Five major image processing steps were used for the early detection and automated classification and counting of colonies. An early stage involves inspecting the morphology of the bacterial colonies. For instance, identifying the species and its antibiotic susceptibility is vital Convolutional Neural Network (CNN) is a class of deep neural networks. Apr 1, 2025 · For deep learning applications on filamentous bacteria classification, we found that the image resolution has to be at least 788 × 530. Feb 1, 2025 · Quantification of morphological characteristics and filamentous bacteria in activated-sludge flocs through quantitative image-analysis techniques incorporating image-processing software and U-Net deep-learning framework Jul 1, 2021 · Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. Experimental results reveal superior accuracy compared to existing baseline methods, showcasing the potential of deep learning for efficient and precise bacteria classification. Bacteria Classification using Image Processing and Deep learning. 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). Through the utilization This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The network initially preprocesses the colony image data using techniques like differential analysis [23]. Furthermore, other machine learning methods except for NN methods already have acceptable performance. In the realm of microbiology and biomedical research, an accurate and rapid classification of bacterial species from microscopic images plays a pivotal role in disease diagnosis and treatment. The focus of this paper is on bacteria detection, identification, and classification. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. However, classification of bacteria, which come in different shapes and sizes and are very small structures, based Treebupachatsakul T, Poomrittigul S (2019) Bacteria classification using image processing and deep learning, 2019 34th International Technical Conference on Circuits/Systems, Computers and This A key component of illness diagnosis is the classification of microorganisms. & Poomrittigul, S. Apr 26, 2024 · Bacteria detection is performed by a novel deep learning-based model with user-specified parameters. Deep learning is a popular approach that commonly used in image processing tasks such as detection, classification, and segmentation. This article applies the state of the art method for texture analysis to classify genera and species of bacteria using deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest. Deep learning has achieved remarkable results in long-standing artificial intelligence tasks in the fields of image processing [21], speech recognition [22], and natural language processing [23]. Thus, regularly the examination needs to be done by experts or specialists to classify the species of them and it also takes long time with the possibility on incorrect recognition. Deep learning has made significant progress in recent years in the area of complex problems originated in image classification area. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to Oct 1, 2018 · Analysis of microscope images is an important topic in medical image processing. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. Jun 7, 2022 · In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Different layers in deep learning are responsible for input, training, classification Aug 8, 2024 · This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. Keywords: Image Processing, Machine Learning, Deep learning, Bacteria classification, MATLAB etc. [12] presented a comprehensive review of CBMIA (Content-based microscopic image analysis) methods, applied in microorganisms’ classification field. Dec 1, 2023 · This paper describes the development of a microscope image dataset for grapevine fungal spore classification using deep learning. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide Sep 8, 2023 · It is a well-known fact that better features are always a guarantee of better classification outcomes in ML and image classification challenges, and image classification is no exception. Oct 12, 2021 · The advent of AI in the field of image classification using different ML techniques gave edge to the different researchers to automate the classification of bacteria based on image analysis. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic image analysis of a wide range of microorganisms, including viruses, bacteria, fungi, and parasites. Typically, classification has been done by clinical specialists using conventional techniques, which do not rely on prediction approaches. Our method includes a pre-processing step to enhance image contrast and reduce noise, followed by a feature extraction step using a pre-trained CNN. Deep learning algorithms, especially CNN, auto-matically learn high-level feature representations from the raw data without requiring any handmade feature extractions. Identification and classification of bacterial genera and species are very important for medical prevention, diagnosis, and treatment. Dec 2, 2024 · We developed a deep learning-based neural network for the detection and classification of four trauma-susceptible bacteria (AB, EC, PA, and SA). By harnessing cutting-edge Convolutional Neural Networks (CNNs) and advanced image processing techniques, our approach not only significantly boosts the efficiency of bacterial species identification but Here, we demonstrate the first use of a TFT-based image sensor to build a real-time CFU detection system to automatically count the bacterial colonies and rapidly identify their species using deep learning. The proposed models can output images with bounding boxes surrounding each … Mar 28, 2025 · However, the limited availability of the bacteria image dataset can be considered an important bottleneck for the development of deep learning model for bacteria detection. In this paper, we review the publications, which investigate the discrimination between bacteria genera and suborders based on macroscopic images via image processing and machine learning methods. However, addressing the considerable data requirements of deep learning, recent advancements encompass the application of pre-trained models using transfer learning for the identification of microbial entities. To improve our ability to detect dangerous bacteria including E. Aug 4, 2024 · This study compares the detection and counting of bacilli images using classical image processing and deep learning to ascertain the performance of both methods. Dec 5, 2023 · Guide to micro-organism image classification using deep learning, detailing data prep, model building, training, and real-world applications in disease diagnosis, drug discovery, and ecology Bacteria Classification using Image Processing and Deep learning Conference Paper Jun 2019 Treesukon Treebupachatsakul Suvit Poomrittigul In this study, we propose a classification method for microscopic images of bacteria using a CNN-based approach. The possibility of using image classification and deep learning methods to recognize the standard and high-resolution bacteria and yeast images is studied in Treebupachatsakul and Poomrittigul (2020). Aug 1, 2024 · Consequently, comprehensive and real-time monitoring of this balance will enable reliable operation of biological wastewater treatment. Accurate and rapid classification of bacterial species is of great importance, particularly early detection and treatment of diseases caused by bacteria. 8793320 Conventional culture-based techniques are still prevalent but are hindered by protracted processes and subjective bacterial classification. Jun 30, 2023 · To automate the entire process, a deep learning based system to classify bacteria images can reduce the difficulties posed by the scientists working in this domain. The research works are compared in terms of the type of images, diversity of bacteria, machine learning, and computer vision techniques, and the privileges and disadvantages of each work are explained. Nov 22, 2019 · Bacteria is one of the reasons that cause many diseases, and the diagnosis is hard to be done because of its shape and complexity. Numerous characteristics, including batch size, edges, shape, and color, support the model's categorization and enhance its capacity to discriminate between This A key component of illness diagnosis is the classification of microorganisms. Dec 15, 2024 · Here, we propose a deep-learning-based spectrogram processing algorithm, named Wavelet Packet transform and Gramian Angular field (WPGA) algorithm, for rapidly and efficiently identifying bacteria based on SERS spectra. Sep 29, 2021 · In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. This work presents twelve fine-tuned deep learning architectures to solve the bacterial classification problem over the Digital Image of Bacterial Species Dataset, and proposes a novel data augmentation technique based on the idea of artificial zooming, strongly increasing the performance of every tested architecture. The acquired lens-free images were processed using custom-developed image processing and deep learning algorithms. The efficiency of conventional textural features and deep features has been investigated for the identification and classification of bacteria from digital Images. jnsoam xld lrykpq kidyq wjj wsv pna hen eydtk xoejbng